ZECO: ZeroFusion Guided 3D MRI Conditional Generation
This addresses the challenge of acquiring precise lesion masks for training segmentation models in clinical practice, which is incremental as it builds on existing generation methods.
The paper tackles the problem of data scarcity in medical image segmentation for MRI by proposing ZECO, a framework that generates high-fidelity 3D MRI images with corresponding segmentation masks, outperforming state-of-the-art models on Brain MRI datasets.
Medical image segmentation is crucial for enhancing diagnostic accuracy and treatment planning in Magnetic Resonance Imaging (MRI). However, acquiring precise lesion masks for segmentation model training demands specialized expertise and significant time investment, leading to a small dataset scale in clinical practice. In this paper, we present ZECO, a ZeroFusion guided 3D MRI conditional generation framework that extracts, compresses, and generates high-fidelity MRI images with corresponding 3D segmentation masks to mitigate data scarcity. To effectively capture inter-slice relationships within volumes, we introduce a Spatial Transformation Module that encodes MRI images into a compact latent space for the diffusion process. Moving beyond unconditional generation, our novel ZeroFusion method progressively maps 3D masks to MRI images in latent space, enabling robust training on limited datasets while avoiding overfitting. ZECO outperforms state-of-the-art models in both quantitative and qualitative evaluations on Brain MRI datasets across various modalities, showcasing its exceptional capability in synthesizing high-quality MRI images conditioned on segmentation masks.